LAPSE:2019.0231
Published Article
LAPSE:2019.0231
A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI
Fengqi Zhang, Haiou Liu, Yuhui Hu, Junqiang Xi
February 5, 2019
This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC) charge-sustainability. First, the relationship between the equivalent factor (EF) of ECMS and the co-state of pontryagin’s minimum principle (PMP) is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI) controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE) cycle, respectively.
Keywords
equivalent consumption minimization strategy, equivalent factor, fuzzy proportional plus integral (PI), hybrid electric vehicle
Suggested Citation
Zhang F, Liu H, Hu Y, Xi J. A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI. (2019). LAPSE:2019.0231
Author Affiliations
Zhang F: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710000, China; School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Liu H: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Hu Y: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Xi J: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
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Journal Name
Energies
Volume
9
Issue
11
Article Number
E919
Year
2016
Publication Date
2016-11-08
Published Version
ISSN
1996-1073
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PII: en9110919, Publication Type: Journal Article
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LAPSE:2019.0231
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doi:10.3390/en9110919
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Calvin Tsay
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